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How to use init_args method in autotest

Best Python code snippet using autotest_python

main.py

Source:main.py

1import argparse2from neural_CR.dataSet import DataGenerator3from neural_CR.dataloader import DataSampler4from neural_CR.nets import NCR5from neural_CR.models import NCRTrainer6from neural_CR.testeval import check_valid, chk_logic7import torch8import pandas as pd9import logging10logging.basicConfig()11logging.getLogger().setLevel(logging.DEBUG)12device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  # set pytorch device for computation13def main():14    arg_par = argparse.ArgumentParser(description='Experiment')15    arg_par.add_argument('--threshold', type=int, default=4,16                             )17    arg_par.add_argument('--order', type=bool, default=True,18                             )19    arg_par.add_argument('--leave_n', type=int, default=1,20                             )21    arg_par.add_argument('--keep_n', type=int, default=5,22                             )23    arg_par.add_argument('--max_history_length', type=int, default=5,24                             )25    arg_par.add_argument('--n_neg_train', type=int, default=1,26                             )27    arg_par.add_argument('--n_neg_val_test', type=int, default=100,28                             )29    arg_par.add_argument('--training_batch_size', type=int, default=128,30                             )31    arg_par.add_argument('--val_test_batch_size', type=int, default=128 * 2,32                             )33    arg_par.add_argument('--seed', type=int, default=2022,34                             )35    arg_par.add_argument('--emb_size', type=int, default=64,36                             )37    arg_par.add_argument('--dropout', type=float, default=0.0,38                             )39    arg_par.add_argument('--lr', type=float, default=0.001,40                             )41    arg_par.add_argument('--l2', type=float, default=0.0001,42                             )43    arg_par.add_argument('--r_weight', type=float, default=0.1,44                             )45    arg_par.add_argument('--val_metric', type=str, default='ndcg@5',46                             )47    arg_par.add_argument('--test_metrics', type=list, default=['ndcg@5', 'ndcg@10', 'hit@5', 'hit@10'],48                             )49    arg_par.add_argument('--n_epochs', type=int, default=100,50                             )51    arg_par.add_argument('--early_stop', type=int, default=5,52                             )53    arg_par.add_argument('--at_least', type=int, default=20,54                             )55    arg_par.add_argument('--save_load_path', type=str, default="saved-models/best_model.json",56                             )57    arg_par.add_argument('--n_times', type=int, default=10,58                             )59    arg_par.add_argument('--dataset', type=str, default="movielens_100k",60                             )61    arg_par.add_argument('--test_only', type=bool, default=False,62                             )63    arg_par.add_argument('--premise_threshold', type=int, default=0,64                             )65    arg_par.add_argument('--remove_double_not', type=bool, default=False,66                             )67    init_args, init_extras = arg_par.parse_known_args()68    if init_args.dataset == "movielens_100k":69        raw_dataset = pd.read_csv("datasets/movielens-100k/movielens_100k.csv")70    dataset = DataGenerator(raw_dataset)71    dataset.manupulate_data(threshold=init_args.threshold, order=init_args.order, leave_n=init_args.leave_n,72                            keep_n=init_args.keep_n, max_history_length=init_args.max_history_length,73                            premise_threshold=init_args.premise_threshold)74    if init_args.test_only:75        pass76    else:77        t_load = DataSampler(dataset.t_set, dataset.user_item_matrix, n_neg_samples=init_args.n_neg_train,78                                   batch_size=init_args.training_batch_size, shuffle=True, seed=init_args.seed,79                                   device=device)80        v_loa = DataSampler(dataset.v_set, dataset.user_item_matrix,81                                 n_neg_samples=init_args.n_neg_val_test, batch_size=init_args.val_test_batch_size,82                                 shuffle=False, seed=init_args.seed, device=device)83    t_loa = DataSampler(dataset.test_set, dataset.user_item_matrix, n_neg_samples=init_args.n_neg_val_test,84                              batch_size=init_args.val_test_batch_size, shuffle=False, seed=init_args.seed,85                              device=device)86    ncr_net = NCR(dataset.all_users, dataset.all_items, emb_size=init_args.emb_size, dropout=init_args.dropout,87                  seed=init_args.seed, remove_double_not=init_args.remove_double_not).to(device)88    ncr_model = NCRTrainer(ncr_net, learning_rate=init_args.lr, l2_weight=init_args.l2,89                           logic_reg_weight=init_args.r_weight)90    if init_args.test_only:91        pass92    else:93        ncr_model.train(t_load, valid_data=v_loa, valid_metric=init_args.val_metric,94                        valid_func=check_valid(chk_logic), num_epochs=init_args.n_epochs,95                        at_least=init_args.at_least, e_stop=init_args.early_stop,96                        save_path=init_args.save_load_path, verbose=1)97    ncr_model.m_load(init_args.save_load_path)98    ncr_model.test(t_loa, test_metrics=init_args.test_metrics, n_times=init_args.n_times)99if __name__ == '__main__':...

test_backends.py

Source:test_backends.py

1import asyncio2import pytest3from tamarco.core.settings.backends import EtcdSettingsBackend4from tests.functional.core.settings.conftest import settings_backends5@pytest.mark.asyncio6@pytest.mark.parametrize("settings_class,init_args", settings_backends)7async def test_settings_get(settings_class, init_args, event_loop, loaded_test_settings):8    settings = settings_class(*init_args)9    redis_port = await settings.get("redis.port")10    assert redis_port == 700611    redis_host = await settings.get("redis.host")12    assert redis_host == "127.0.0.1"13    redis_conf = await settings.get("redis")14    assert redis_conf["host"] == "127.0.0.1"15    elasticsearch = await settings.get("elasticsearch")16    assert len(elasticsearch) == 217    assert all("127.0.0.1" in x for x in elasticsearch)18    not_a_key = await settings.get("not_a_key", default=99)19    assert not_a_key == 9920@pytest.mark.asyncio21@pytest.mark.parametrize("settings_class,init_args", settings_backends)22async def test_settings_get_wrong_key(settings_class, init_args, event_loop):23    settings = settings_class(loop=event_loop, *init_args)24    with pytest.raises(KeyError):25        await settings.get("non_existing_key")26@pytest.mark.asyncio27@pytest.mark.parametrize("settings_class,init_args", settings_backends)28async def test_settings_get_multiple_dict_level(settings_class, init_args, event_loop, loaded_test_settings):29    settings = settings_class(loop=event_loop, *init_args)30    signaler = await settings.get("signaler")31    assert signaler["redis"]["port"] == 700632@pytest.mark.asyncio33@pytest.mark.parametrize("settings_class,init_args", settings_backends)34async def test_settings_set(settings_class, init_args, event_loop, add_logging):35    settings = settings_class(loop=event_loop, *init_args)36    await settings.set("redis.port", 9999)37    assert (await settings.get("redis.port")) == 999938    await settings.set("redis.host", "127.0.0.2")39    assert (await settings.get("redis.host")) == "127.0.0.2"40    await settings.set("logging.test.loggers", "StreamLogger")41    assert (await settings.get("logging.test.loggers")) == "StreamLogger"42@pytest.mark.asyncio43@pytest.mark.parametrize("settings_class,init_args", settings_backends)44async def test_settings_delete(settings_class, init_args, event_loop, loaded_test_settings):45    settings = settings_class(loop=event_loop, *init_args)46    await settings.delete("redis.port")47    with pytest.raises(KeyError):48        await settings.get("redis.port")49    await settings.delete("elasticsearch")50    with pytest.raises(KeyError):51        await settings.get("elasticsearch")52@pytest.mark.asyncio53@pytest.mark.parametrize("settings_class,init_args", settings_backends)54async def test_settings_watch_set(settings_class, init_args, event_loop):55    settings = settings_class(loop=event_loop, *init_args)56    fut = asyncio.Future()57    async def callback(key, value):58        fut.set_result((key, value))59    await settings.set("redis.port", 8888)60    await settings.watch("redis.port", callback)61    await asyncio.sleep(0.1)62    await settings.set("redis.port", 9999)63    key, value = await fut64    assert key == "redis.port"65    assert value == 999966@pytest.mark.asyncio67@pytest.mark.parametrize("settings_class,init_args", settings_backends)68async def test_settings_watch_delete(settings_class, init_args, event_loop):69    settings = settings_class(loop=event_loop, *init_args)70    fut = asyncio.Future()71    async def callback(key, value):72        fut.set_result((key, value))73    await settings.watch("redis.port", callback)74    await asyncio.sleep(0.1)75    await settings.delete("redis.port")76    key, value = await fut77    assert key == "redis.port"78    assert value is None79@pytest.mark.asyncio80async def test_etcd_settings_check_machines():81    backend = EtcdSettingsBackend({"host": "127.0.0.1", "port": 2379})...

multiple_dispatch.py

Source:multiple_dispatch.py

1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Utilities for type-dependent behavior used in py2tf-generated code."""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19import six20from tensorflow.contrib.py2tf.utils.type_check import is_tensor21from tensorflow.python.ops import control_flow_ops22def run_cond(condition, true_fn, false_fn):23  """Type-dependent functional conditional.24  Args:25    condition: A Tensor or Python bool.26    true_fn: A Python callable implementing the true branch of the conditional.27    false_fn: A Python callable implementing the false branch of the28      conditional.29  Returns:30    result: The result of calling the appropriate branch. If condition is a31    Tensor, tf.cond will be used. Otherwise, a standard Python if statement will32    be ran.33  """34  if is_tensor(condition):35    return control_flow_ops.cond(condition, true_fn, false_fn)36  else:37    return py_cond(condition, true_fn, false_fn)38def py_cond(condition, true_fn, false_fn):39  if condition:40    return true_fn()41  else:42    return false_fn()43def run_while(cond_fn, body_fn, init_args):44  """Type-dependent functional while loop.45  Args:46    cond_fn: A Python callable implementing the stop conditions of the loop.47    body_fn: A Python callable implementing the body of the loop.48    init_args: The initial values of the arguments that will be passed to both49      cond_fn and body_fn.50  Returns:51    result: A list of values with the same shape and type as init_args. If any52    of the init_args, or any variables closed-over in cond_fn are Tensors,53    tf.while_loop will be used, otherwise a Python while loop will be ran.54  Raises:55    ValueError: if init_args is not a tuple or list with one or more elements.56  """57  if not isinstance(init_args, (tuple, list)) or not init_args:58    raise ValueError(59        'init_args must be a non-empty list or tuple, found %s' % init_args)60  # TODO(alexbw): statically determine all active variables in cond_fn,61  # and pass them directly62  closure_vars = tuple(63      [c.cell_contents for c in six.get_function_closure(cond_fn) or []])64  possibly_tensors = tuple(init_args) + closure_vars65  if is_tensor(*possibly_tensors):66    return control_flow_ops.while_loop(cond_fn, body_fn, init_args)67  else:68    return py_while_loop(cond_fn, body_fn, init_args)69def py_while_loop(cond_fn, body_fn, init_args):70  state = init_args71  while cond_fn(*state):72    state = body_fn(*state)...

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